Drainage has turned peatlands from a carbon sink into one of the world's largest greenhouse gas (GHG) sources from cultivated soils. We analyzed a unique data set (12 peatlands, 48 sites and 122 annual budgets) of mainly unpublished GHG emissions from grasslands on bog and fen peat as well as other soils rich in soil organic carbon (SOC) in Germany. Emissions and environmental variables were measured with identical methods. Site-averaged GHG budgets were surprisingly variable (29.2 ± 17.4 t CO -eq. ha yr ) and partially higher than all published data and the IPCC default emission factors for GHG inventories. Generally, CO (27.7 ± 17.3 t CO ha yr ) dominated the GHG budget. Nitrous oxide (2.3 ± 2.4 kg N O-N ha yr ) and methane emissions (30.8 ± 69.8 kg CH -C ha yr ) were lower than expected except for CH emissions from nutrient-poor acidic sites. At single peatlands, CO emissions clearly increased with deeper mean water table depth (WTD), but there was no general dependency of CO on WTD for the complete data set. Thus, regionalization of CO emissions by WTD only will remain uncertain. WTD dynamics explained some of the differences between peatlands as sites which became very dry during summer showed lower emissions. We introduced the aerated nitrogen stock (N ) as a variable combining soil nitrogen stocks with WTD. CO increased with N across peatlands. Soils with comparatively low SOC concentrations showed as high CO emissions as true peat soils because N was similar. N O emissions were controlled by the WTD dynamics and the nitrogen content of the topsoil. CH emissions can be well described by WTD and ponding duration during summer. Our results can help both to improve GHG emission reporting and to prioritize and plan emission reduction measures for peat and similar soils at different scales.

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Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland-specific land surface hydrology module (PEAT-CLSM) to the Catchment Land Surface Model (CLSM) of the NASA Goddard Earth Observing System (GEOS) framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland-specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT-CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT-CLSM are based on literature data. A suite of CLSM and PEAT-CLSM simulations for peatland areas between 40°N and 75°N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM's simulated groundwater tables are too deep and variable, whereas PEAT-CLSM simulates a mean groundwater table depth of −0.20 m (snow-free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT-CLSM, runoff is increased on average by 38% and evapotranspiration is reduced by 19%. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements.Plain Language Summary Peatlands are wetlands in which plant matter has accumulated over thousands of years under almost permanently water-logged conditions. Alterations in these conditions as a result of global climate change can lead to the release of the huge peatland carbon pool as carbon dioxide over much shorter timescales than were required for accumulation. The additional emissions would amplify global warming. A better representation of the peatland hydrology in global Earth system models can help quantify how peatlands respond to a changing climate. In this paper, we add a peatland-specific land surface hydrology module to the land surface model used in NASA's GEOS Earth

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Accurate and effective determination of soil bulk density (BD) is needed to monitor soil organic C (SOC) stocks and SOC stock changes. However, BD measurements are often lacking in soil inventories and BD is estimated by pedotransfer functions with substantial uncertainty. In a systematic method comparison, we evaluated different methods for BD determination in the field by comparing the performance of MINI (5 cm 3 ) and BIG (250 cm 3 ) sample rings and of three driving hammer probes differing in diameter, material, and extraction method. Bulk density determined with 100-cm 3 sample rings was defined as the reference method (REF). All methods were tested at five depth increments in nine subplots at four sites with differing soil texture and SOC content. All methods determined BD in the depth increments with low systematic error (8% for probes and 2% for sample rings). The random error of the probe samples was, on average, 50% higher than that of the ring samples when the cores of the probes were adequately corrected for compaction or stretching. The BD was significantly overestimated (by 2%) when determined with MINI rings, and the variation in BD was not reduced with BIG sample rings rather than the smaller REF sample rings. The performance of the driving hammer technique varied widely among probe types and sites. The sheath probe had the smallest systematic error of all probes tested and is recommended for soil inventories. All methods for estimating BD had smaller errors than pedotransfer functions.Abbreviations: BD, bulk density; CV, coefficient of variation; MPE, mean prediction error; SDPE, standard deviation of the prediction error; SOC, soil organic carbon. C arbon storage in soils exceeds that in vegetation and the atmosphere (Ciais et al., 2013). Thus, small changes in soil organic C (SOC) stocks could have severe impacts on the global C cycle. Reliable measurements of C concentration are an important prerequisite for detecting such small changes in SOC stocks (Goidts et al., 2009). Information on soil bulk density (BD) is essential in converting weight-based concentration data to volume-or area-based stock data. However, BD is a parameter that is only partly or never sampled in many soil inventories (Gruneberg et al., 2014;Reynolds et al., 2013;Saby et al., 2008). Pedotransfer functions are often applied instead to predict soil BD on the basis of SOC or soil organic matter content and soil texture data (Arrouays et al., 2012). It has been shown that most pedotransfer functions are suitable only for the agro-pedo-climatic conditions prevailing at the sites used to fit these functions (Martin et al., 2009). Under different conditions, they lead to substantial systematic errors (De Vos et al., 2005;Nanko et al., 2014;Vasiliniuc and Patriche, 2015). For a soil with a BD of 1.4 g cm −3 , a systematic measurement error of −0.01 to −0.51 g cm −3 (De Vos et al., 2005) would result in SOC stocks being underestimated by 1 to 36%. Katja Core Ideas• Little is known about the methodological errors of bulk densit...